Abstract: Alzheimer's disease (AD), also referred to simply as Alzheimer's, is a chronic neurodegenerative disease that usually starts slowly and worsens over time. It is the cause of 60% to 70% of cases of dementia. In 2015, there were approximately 29.8 million people worldwide with AD. It most often begins in people over 65 years of age as it affects about 6% of people 65 years and older, although 4% to 5% of cases are early-onset Alzheimer's which begin before this. In 2015, researchers have figured out that dementia resulted in about 1.9 million deaths. Continuous efforts are made to cure the disease or to delay its progression. Brain imaging is one of the hottest areas in AD research. Techniques like CT, MRI, SPECT, and PET assist in disease detection and help in excluding other probable causes of dementia. Imaging helps to perceive the intended cause of the disease as well as track the disease through its course. This paper applies Image processing and machine learning techniques combined to MRI brain images to help in detection of AD and classify the case either to MDI or Dementia.

Nermin Negied and Ahmed SeragEldin, “Automatic Detection of Alzheimer Disease from 3D MRI Images using Deep CNNs” International Journal of Advanced Computer Science and Applications(IJACSA), 13(12), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0131258

@article{Negied2022,
title = {Automatic Detection of Alzheimer Disease from 3D MRI Images using Deep CNNs},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0131258},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0131258},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {12},
author = {Nermin Negied and Ahmed SeragEldin}
}